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Record W4414402726 · doi:10.1111/pere.70031

A Multi‐Level Meta‐Analysis Comparing Relationship Norm Strength of <scp>LGB</scp> and Heterosexual Relationships

2025· article· en· W4414402726 on OpenAlexaff
Maximiliane Uhlich, John Kitchener Sakaluk

Bibliographic record

VenuePersonal Relationships · 2025
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsWestern University
Fundersnot available
KeywordsNorm (philosophy)NormativeSexual orientationOppressionMultilevel modelSexual relationshipHeterosexuality

Abstract

fetched live from OpenAlex

ABSTRACT LGB people have historical and contemporary experiences of discrimination, stigma, and violence. Research on social groups suggests that norms, rules, and expectations for group conduct emerge to provide responses to group threats, for example, oppression of LGB individuals, and arise from cultural expectations and agreements between partners. While previous research demonstrated that norm content , specific rules, and expectations in relationships established by LGB people differ from their heterosexual counterparts, it is unclear if norm strength (i.e., when norms are clearly articulated, shared, binding) and its role for relationship satisfaction vary across sexual orientations. Using multilevel meta‐analysis, we synthesized effects from four independent samples (total N = 1171) containing data on sexual orientation, relationship norm strength, and satisfaction. We tested whether (a) norm strength and (b) its association with satisfaction were dependent on sexual orientation and/or particular forms of norm strength. Contrary to expectations, we found no differences in norm strength across sexual identities or forms of norm strength. Our findings suggest similar degrees of norm strength govern LGB relationships. Limitations include cross‐sectional MTurk samples and mono‐informant data that prevent examination of dyadic processes. Future research should distinguish which normative mechanisms (e.g., the content of/conformity to norms) are responsible for shaping relationships.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.053
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.317
GPT teacher head0.404
Teacher spread0.087 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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